{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T14:30:22Z","timestamp":1742913022818,"version":"3.40.3"},"publisher-location":"Cham","reference-count":15,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030622220"},{"type":"electronic","value":"9783030622237"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-62223-7_3","type":"book-chapter","created":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:03:00Z","timestamp":1605002580000},"page":"26-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Anomalous Traffic Detection Approach for the Private Network Based on Self-learning Model"],"prefix":"10.1007","author":[{"given":"Weijie","family":"Han","sequence":"first","affiliation":[]},{"given":"Jingfeng","family":"Xue","sequence":"additional","affiliation":[]},{"given":"Fuquan","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yingfeng","family":"Zhang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,11,11]]},"reference":[{"key":"3_CR1","doi-asserted-by":"publisher","unstructured":"Hasan, M.S., ElShakankiry, A., Dean, T., Zulkernine, M.: Intrusion detection in a private network by satisfying constraints. In: 2016 14th Annual Conference on Privacy, Security and Trust. Auckland, New Zealand, 12\u201314 December 2016. https:\/\/doi.org\/10.1109\/PST.2016.7906997","DOI":"10.1109\/PST.2016.7906997"},{"issue":"2","key":"3_CR2","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1049\/iet-ifs.2018.5186","volume":"13","author":"W Han","year":"2019","unstructured":"Han, W., Xue, J., Yan, H.: Detecting anomalous traffic in the controlled network based on cross entropy and support vector machine. IET Inf. Secur. 13(2), 109\u2013116 (2019). https:\/\/doi.org\/10.1049\/iet-ifs.2018.5186","journal-title":"IET Inf. Secur."},{"key":"3_CR3","doi-asserted-by":"publisher","unstructured":"Vijayasarathy, R., Raghavan, S.V., Ravindran, B.: A system approach to network modeling for DDoS detection using a Na\u00ecve Bayesian classifier. In: Proceedings of 2011 Third International Conference on Communication Systems and Networks, Bangalore, India, 4\u20138 January 2011. https:\/\/doi.org\/10.1109\/COMSNETS.2011.5716474","DOI":"10.1109\/COMSNETS.2011.5716474"},{"key":"3_CR4","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1016\/j.eswa.2016.07.036","volume":"64","author":"M Swarnkar","year":"2016","unstructured":"Swarnkar, M., Hubballi, N.: OCPAD: one class Naive Bayes classifier for payload based anomaly detection. Expert Syst. Appl. 64, 330\u2013339 (2016)","journal-title":"Expert Syst. Appl."},{"key":"3_CR5","doi-asserted-by":"publisher","unstructured":"Li, W., Li, Q.X.: Using Naive Bayes with AdaBoost to enhance network anomaly intrusion detection. In: Proceedings of International Conference on Intelligent Networks & Intelligent Systems, pp. 486\u2013489. IEEE Computer Society (2010). https:\/\/doi.org\/10.1109\/ICINIS.2010.133","DOI":"10.1109\/ICINIS.2010.133"},{"issue":"1","key":"3_CR6","first-page":"14","volume":"1","author":"DK Ahirwar","year":"2012","unstructured":"Ahirwar, D.K., Saxena, S.K., Sisodia, M.S.: Anomaly detection by Naive Bayes & RBF network. Int. J. Adv. Res. Comput. Sci. Electron. Eng. 1(1), 14\u201318 (2012)","journal-title":"Int. J. Adv. Res. Comput. Sci. Electron. Eng."},{"issue":"2","key":"3_CR7","doi-asserted-by":"publisher","first-page":"653","DOI":"10.12785\/amis\/092L41","volume":"9","author":"T Peng","year":"2015","unstructured":"Peng, T., Tang, Z.: A small scale forecasting algorithm for network traffic based on relevant local least squares support vector machine regression model. Appl. Math. Inf. Sci. 9(2), 653\u2013659 (2015). https:\/\/doi.org\/10.12785\/amis\/092L41","journal-title":"Appl. Math. Inf. Sci."},{"issue":"2","key":"3_CR8","doi-asserted-by":"publisher","first-page":"1822","DOI":"10.1016\/j.eswa.2011.08.068","volume":"39","author":"CA Catania","year":"2010","unstructured":"Catania, C.A., Bromberg, F., Garino, C.G.: An autonomous labeling approach to support vector machines algorithms for network traffic anomaly detection. Expert Syst. Appl. 39(2), 1822\u20131829 (2010)","journal-title":"Expert Syst. Appl."},{"key":"3_CR9","doi-asserted-by":"publisher","unstructured":"Ji, S.Y., Choi, S., Dong, H.J.: Designing a two-level monitoring method to detect network anomalous behaviors. In: Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration, Redwood City, CA, USA, 13\u201315 August 2014. IEEE (2014). https:\/\/doi.org\/10.1109\/IRI.2014.7051958","DOI":"10.1109\/IRI.2014.7051958"},{"key":"3_CR10","unstructured":"Li, S., Yun, X., Zhang, Y.: A model of trojan communication behavior detection based on hierarchical clustering technique. Comput. Res. Dev. (s2), 9\u201316 (2012)"},{"key":"3_CR11","unstructured":"Yu, H., Wang, J.: Analysis of network traffic based on IP address clustering. J. Ocean Univ. China Nat. Sci. Ed. (s1), 196\u2013199 (2008)"},{"key":"3_CR12","unstructured":"Wang, X., Liang, X.: Network traffic prediction model based on BPSO-RBFNN. Comput. Appl. Softw. (9), 102\u2013105 (2014)"},{"issue":"39","key":"3_CR13","doi-asserted-by":"publisher","first-page":"428","DOI":"10.1016\/j.procs.2015.03.174","volume":"45","author":"U Ravale","year":"2015","unstructured":"Ravale, U., Marathe, N., Padiya, P.: Feature selection based hybrid anomaly intrusion detection system using k means and RBF kernel function. Procedia Comput. Sci. 45(39), 428\u2013435 (2015)","journal-title":"Procedia Comput. Sci."},{"issue":"1","key":"3_CR14","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/s13177-018-0167-5","volume":"18","author":"S Lykov","year":"2018","unstructured":"Lykov, S., Asakura, Y.: Anomalous traffic pattern detection in large urban areas: tensor-based approach with continuum modeling of traffic flow. Int. J. Intell. Transp. Syst. Res. 18(1), 13\u201321 (2018). https:\/\/doi.org\/10.1007\/s13177-018-0167-5","journal-title":"Int. J. Intell. Transp. Syst. Res."},{"issue":"6","key":"3_CR15","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1109\/TSP.2019.2892026","volume":"67","author":"E Hou","year":"2019","unstructured":"Hou, E., Y\u0131lmaz, Y., Hero, A.O.: Anomaly detection in partially observed traffic networks. IEEE Trans. Signal Process. 67(6), 1461\u20131476 (2019). https:\/\/doi.org\/10.1109\/TSP.2019.2892026","journal-title":"IEEE Trans. Signal Process."}],"container-title":["Lecture Notes in Computer Science","Machine Learning for Cyber Security"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-62223-7_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,10]],"date-time":"2020-11-10T10:06:24Z","timestamp":1605002784000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-62223-7_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030622220","9783030622237"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-62223-7_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"11 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ML4CS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Machine Learning for Cyber Security","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ml4cs2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/nsclab.org\/ml4cs2020\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"360","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"118","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"40","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"33% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"8","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}